Engineered nanomaterials (ENMs) have novel physical and chemical properties, stemming from their nanometer-scale size, and can undergo dynamic changes (e.g., agglomeration, aggregation, and oxidation) when interacting with biological systems. This interaction, thus, has important implications for ENM health effects. The proposed Center grant, entitled 'Respiratory Effects of Silver and Carbon Nanomaterials (RESAC)', focusing on the interaction between ENMs and the lung lining fluid, will use a systematic, integrated, multidisciplinary approach to produce mechanism-driven toxicological data that will be used in a mechanism-based risk analysis framework for ENMs. Main research of the Center will be carried out through three inter-related projects. Project 1 will use cell models to determine ENM physicochemical properties (e.g., size, shape, aspect ratio, chemistry) that dictate the molecular, cellular, and immune reactivities in response to ENM interactions with the lung lining fluid. It is expected that such interaction will also critically impact ENM physicochemical properties, which in turn, will impact on the ENM reactivity with macrophages and epithelial cells. These early cellular- and molecular-level events are crucial in predicting the ultimate fate and pathological consequences of inhaled ENMs, which will be studied in Project 2 using rat and mouse models. Project 2 will examine whether and how inhaled ENMs affect lung surfactant composition and function, induce lung inflammation and lung pathology, generate oxidative stress, penetrate lung cells, and affect the innate immune function of macrophages. Results generated from Projects 1 and 2, along with other relevant data to be collected, will be used in Project 3 that is to define, demonstrate, and test a prototype generalized risk analysis framework for ENMs by implementing, adapting, and expanding available state-of-the-art multi-scale modeling systems for the exposure-to-dose-to-effect sequence. Our new models will mathematically link specific ENM properties (e.g., surface area) to biological effects, allowing the use of most relevant 'dosimetry' in predicting ENM health risks.